Optimizing Electric Vehicle Charging Through an Artificial Intelligence Mechanism for Smart Transportation

Artificial intelligence of vehicles (AIVs) is poised to revolutionize transportation by promoting low-carbon alternatives, such as electric vehicles (EVs). However, the deployment of fixed charging stations (FCSs) lags behind the growing demand, particularly in rural areas, causing range anxiety among potential EV owners. This article proposes a smart transportation solution within the Artificial Intelligence of Things (AIoT) framework to establish a sustainable, low-carbon system. AIoT systems enable real-time data acquisition and analysis through extensive embedded IoT EV sensors and communication networks for pattern recognition and decision making on the cloud. The proposed solution integrates sensor information from vehicle-to-vehicle (V2V) charging, smart home charging stations (HCSs), and mobile charging services (MCSs), coordinated by the cloud-fog nodes in geographically distributed zones. This article employs the Hungarian matching algorithm for optimal decision making of matching EVs with charging services. Our approach incorporates AIV and AIoT technologies to enhance decision making by using an ensemble-based machine learning (ML) model for precise EV range estimation. The comprehensive details and specifications of these proposed models are elaborated in this article.